Search Results (93)

Reliable monitoring of spontaneous affect is essential in biomedical sensing, where involuntary facial signals serve as objective indicators of physiological states. Micro expression recognition (MER) is particularly challenging due to the sub-second, low amplitude nature of these signals. Many existing MER methods rely on apex (peak) frame detection, making them sensitive to temporal localization errors and difficult to deploy in unconstrained settings. To address this, we propose an apex-free framework that analyzes facial dynamics by structuring motion-magnified features along a newly introduced magnification intensity axis. By applying Eulerian motion magnification across multiple discrete levels and collapsing the sequences into single accumulation images, we generate a multi-level representation of subtle facial dynamics without requiring frame-level annotations. A lightweight shared temporal mixer (STM) is employed to analyze the dynamic evolution of motion across the magnification intensity axis. Subsequently, a dual-branch convolutional neural network (CNN), processing low- and high-amplification regimes respectively, integrates a convolutional block attention module (CBAM) to capture subtle facial motion while effectively filtering out irrelevant noise. Our model is highly efficient, requiring only 0.94 M parameters and 262 MFLOPs, which is significantly lower than the computational demands of standard backbones such as ResNet18 or VGG16. To ensure the model generalizes to new individuals, we evaluated it by testing on subjects whose data was entirely excluded from the training process. Under this rigorous setup, the proposed method achieves approximately 80% and 70% accuracy on the CASME II and SMIC datasets respectively, showing performance comparable to, or in some cases, slightly above current state-of-the-art methods. Considering both the competitive accuracy and high computational efficiency, the proposed framework holds significant potential for practical integration into real-time affect monitoring systems, particularly within biomedical applications. Full article

Micro-expression recognition (MER) remains a formidable challenge in affective computing due to the subtle, localized, and fleeting nature of facial muscle actuations. Conventional spatial-temporal networks are easily overwhelmed by static facial topologies, leading to feature representations that are heavily biased toward identity-specific noise. To address this, we propose the Motion-Anchored Cross-Modal Fusion Network (MACFN), a novel dual-stream ViT architecture that explicitly decouples and synergizes spatial appearance and optical flow dynamics. Specifically, we introduce a motion-anchored spatial attention module, which translates latent motion features into a sparse spatial probability mask. It acts as an enhancement gate, forcing the texture stream to bypass static backgrounds and attend to genuine ME-related regions. Furthermore, we design a cross-modal bilinear fusion module to capture the second-order interactions across modalities, mapping the coupled features into a discriminative semantic manifold. Extensive experiments conducted on the CASME II, SAMM, and SMIC databases under the rigorous leave-one-subject-out composite database evaluation protocol demonstrate that MACFN is effective and achieves competitive performance compared to several recent methods. Full article

Micro-expressions, subtle and often asymmetric facial movements, play a pivotal role in nonverbal emotional communication. Addressing the core challenges of temporal misalignment, fragmented feature extraction, and slow real-time detection in micro-expression recognition (MER), we propose a novel dual-branch spatiotemporal model for dynamic sequence MER. Leveraging MediaPipe for 3D facial feature extraction and Dynamic Time Warping (DTW) for sequence alignment, our method nonlinearly maps variable-length sequences to a fixed length. A hybrid data augmentation technique enhances model robustness, while the dual-branch network simultaneously captures local spatial features and global temporal dynamics. Experimental results on the CASMEII dataset demonstrate state-of-the-art performance with 99.22% accuracy, along with a significant improvement in real-time detection speed. This approach holds substantial practical value for applications in deception detection, mental health assessment, and human–computer interaction. Full article

Background: Trigeminal neuralgia (TN) is a severe neuropathic pain disorder primarily diagnosed on clinical grounds, and objective molecular biomarkers that could support diagnosis remain limited. Increasing evidence suggests that inflammation–apoptosis interactions contribute to TN pathophysiology. Methods: In this exploratory prospective case–control study, circulating apoptosis-related microRNAs (miRNAs) were analyzed in 30 patients with idiopathic TN and 20 healthy controls. Plasma miRNA expression levels were quantified using quantitative real-time polymerase chain reaction. Diagnostic performance of individual miRNAs was assessed using receiver operating characteristic (ROC) curve analysis. A multivariable logistic regression model integrating multiple miRNAs was constructed to evaluate combined diagnostic performance, with internal validation performed using five-fold cross-validation. Results: Circulating miRNA expression profiles differed between TN patients and controls. Among individual markers, hsa-miR-183-5p demonstrated the highest diagnostic accuracy (AUC = 0.72), followed by hsa-miR-23a-3p (AUC = 0.65). hsa-miR-223-3p showed reversed directionality (AUC = 0.28), consistent with lower expression in TN and high specificity but low sensitivity at the optimal threshold. The combined miRNA panel achieved an apparent AUC of 0.86, with a mean cross-validated AUC of 0.84 ± 0.12, suggesting improved discrimination over single miRNAs but with variability consistent with the limited sample size. Conclusions: Apoptosis-related circulating miRNAs exhibit distinct expression patterns in idiopathic TN. While individual miRNAs show modest diagnostic performance, integration into a multi-miRNA panel improved discrimination between TN patients and healthy controls in this pilot dataset. These findings support the potential of apoptosis-based miRNA signatures as candidate minimally invasive biomarkers for TN, warranting further validation in larger, independent cohorts, ideally including clinically relevant disease-control facial pain conditions. Full article

With applications in psychology, security, and human–computer interaction, facial expression recognition (FER) has become an essential tool for non-verbal communication. Current research often categorizes expressions into micro- and macro-types, yet existing datasets suffer from inconsistent labelling for classes, limited diversity of the databases, and insufficient scale for the currently available datasets. To address these gaps, this work proposes a novel framework combining the diffusion model with pre-trained CNNs. Leveraging original images from established datasets, CASME II, we generate synthetic facial expressions to augment training data, mitigating bias and inconsistency. The synthetic dataset is evaluated using ResNet 50, VGG16 and Inception V3 architectures. Inception V3 trained on the proposed AI-generated dataset and tested using CASME II, VGG-16 with data augmentation applied is trained on CASME II and tested on the proposed AI-generated dataset, and Inception V3 with 30% freezing layers method is trained on the proposed AI-generated dataset and tested using CASME II. These all successfully achieved state-of-the-art performance. The data augmentation and freezing layers approaches significantly improved the performance of the models. Our proposed approaches achieved state-of-the-art performance and outperformed most of the existing state-of-the-art approaches benchmarked in this study. Full article

Deep learning models typically rely on large-scale datasets with accurate annotations, yet real-world applications inevitably suffer from label noise, which severely degrades generalization—particularly for lightweight neural networks with limited capacity. Existing learning with noisy labels methods are mainly designed for over-parameterized models and are often unsuitable for resource-constrained deployment. To address this challenge, we propose a robust framework that integrates a Micro Hybrid Attention Module (MHAM) with knowledge distillation (KD) for lightweight architectures such as MobileNetV3. MHAM employs a decoupled channel–spatial attention design to enhance discriminative feature extraction while suppressing noise-sensitive background responses. From a graph–signal perspective, MHAM can be interpreted as a spectral smoothing operator that improves optimization stability. In addition, knowledge distillation with soft teacher supervision mitigates overfitting to corrupted hard labels and reduces prediction uncertainty. Extensive experiments demonstrate the effectiveness of the proposed method. On FER2013, a real-world noisy facial expression recognition benchmark, our approach achieves 68.5% accuracy with only 0.52M parameters, while reducing optimization variance by 24%. On CIFAR-10 with 40% symmetric label noise, it improves accuracy from 54.85% to 60.10%. On CIFAR-10N with multiple types of real-world human annotation noise, the proposed method consistently achieves 63.9–71.9% accuracy under different noise protocols. These results show that the proposed framework provides an efficient and robust solution for noisy label learning in lightweight facial expression and object classification on edge devices. Full article

Micro-expressions (MEs) are involuntary facial movements that reveal genuine emotions, holding significant value in fields like deception detection and psychological diagnosis. However, micro-expression recognition (MER) is fundamentally challenged by the entanglement of subtle emotional motions with identity-specific features. Traditional methods, such as those based on Robust Principal Component Analysis (RPCA), attempt to separate identity and motion components through fixed preprocessing and coarse decomposition. However, these methods can inadvertently remove subtle emotional cues and are disconnected from subsequent module training, limiting the discriminative power of features. Inspired by the Bruce–Young model of facial cognition, which suggests that facial identity and expression are processed via independent neural routes, we recognize the need for a more dynamic, learnable disentanglement paradigm for MER. We propose LFD-TCMEN, a novel network that introduces an end-to-end learnable feature disentanglement framework. The network is synergistically optimized by a multi-task objective unifying orthogonality, reconstruction, consistency, cycle, identity, and classification losses. Specifically, the Disentangle Representation Learning (DRL) module adaptively isolates pure motion patterns from subject-specific appearance, overcoming the limitations of static preprocessing, while the Temporal-Complemented Motion Enhancement (TCME) module integrates purified motion representations—highlighting subtle facial muscle activations—with optical flow dynamics to comprehensively model the spatiotemporal evolution of MEs. Extensive experiments on CAS(ME)³ and DFME benchmarks demonstrate that our method achieves state-of-the-art cross-subject performance, validating the efficacy of the proposed learnable disentanglement and synergistic optimization. Full article

With the growing development of facial micro-expression recognition technology, its practical application value has attracted increasing attention. In real-world scenarios, facial micro-expression recognition typically involves cross-dataset evaluation, where training and testing samples come from different datasets. Specifically, cross-dataset micro-expression recognition employs multi-dataset composite training and unseen single-dataset testing. This setup introduces two major challenges: inconsistent feature distributions across training sets and data imbalance. To address the distribution discrepancy of the same category across different training datasets, we propose a plug-and-play batch regularization learning module that constrains weight discrepancies across datasets through information-theoretic regularization, facilitating the learning of domain-invariant representations while preventing overfitting to specific source domains. To mitigate the data imbalance issue, we propose an Action Unit (AU)-guided generative adversarial network (GAN) for synthesizing micro-expression samples. This approach uses K-means clustering to obtain cluster centers of AU intensities for each category, which are then used to guide the GAN in generating balanced micro-expression samples. To validate the effectiveness of the proposed methods, extensive experiments are conducted on CNN, ResNet, and PoolFormer architectures. The results demonstrate that our approach achieves superior performance in cross-dataset recognition compared to state-of-the-art methods. Full article

This paper proposes E-MOTE (Emotion-aware Teacher Education Framework), an ethically grounded conceptual model aimed at enhancing teacher education through the integrated use of the Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR). As a conceptual and design-oriented proposal, E-MOTE is presented as a structured blueprint for future development and empirical validation, not as an implemented or evaluated system. Grounded in neuroscientific and educational research, E-MOTE seeks to strengthen teachers’ emotional awareness, teacher noticing, and social–emotional learning competencies. Rather than reporting empirical findings, this article offers a theoretically structured framework and an operational blueprint for the design of emotion-aware teacher training environments, establishing a structured foundation for future empirical validation. E-MOTE articulates three core contributions: (1) it clarifies the multi-layered construct of emotion-aware teaching by distinguishing between emotion detection, perception, awareness, and regulation; (2) it proposes an integrated AI–FACS–VR architecture for real-time and post hoc feedback on teachers’ perceptual performance; and (3) it outlines a staged experimental blueprint for future empirical validation under ethically governed conditions. As a design-oriented proposal, E-MOTE provides a structured foundation for cultivating emotionally responsive pedagogy and inclusive classroom management, supporting the development of perceptual micro-skills in teacher practice. Its distinctive contribution lies in proposing a shift from predominantly macro-behavioral simulation toward the deliberate cultivation of perceptual micro-skills through FACS-informed analytics integrated with AI-driven simulations. Full article

Micro-expression recognition (MER) is challenged by a brief duration, low intensity, and heterogeneous spatial frequency patterns. This study introduces a novel MER architecture that reduces computational cost by fine-tuning a large feature extraction model with LoRA, while integrating frequency-domain transformation and graph-based temporal modeling to minimize preprocessing requirements. A Spatial Frequency Adaptive (SFA) module decomposes high- and low-frequency information with dynamic weighting to enhance sensitivity to subtle facial texture variations. A Dynamic Graph Attention Temporal (DGAT) network models video frames as a graph, combining Graph Attention Networks and LSTM with frequency-guided attention for temporal feature fusion. Experiments on the SAMM, CASME II, and SMIC datasets demonstrate superior performance over existing methods. On the SAMM 5-class setting, the proposed approach achieves an unweighted F1 score (UF1) of 81.16% and an unweighted average recall (UAR) of 85.37%, outperforming the next best method by 0.96% and 2.27%, respectively. Full article

Prolonged +Gz acceleration during high-performance flight exposes pilots to the risk of G-induced loss of consciousness (G-LOC), a dangerous condition that compromises operational safety. To enable early detection without intrusive sensors, we present a vision-based warning system that analyzes facial action units (AUs) as physiological indicators of impending G-LOC. Our approach combines computer vision with physiological modeling to capture subtle facial microexpressions associated with cerebral hypoxia using widely available RGB cameras. We propose a novel Graph Convolution-Augmented Vision Transformer (GC-ViT) network architecture that effectively captures dynamic AU variations in pilots under G-LOC conditions by integrating global context modeling with vision Transformer. The proposed framework integrates a vision–semantics collaborative Transformer for robust AU feature extraction, where EfficientNet-based spatiotemporal modeling is enhanced by Transformer attention mechanisms to maintain recognition accuracy under high-G stress. Building upon this, we develop a graph-based physiological model that dynamically tracks interactions between critical AUs during G-LOC progression by learning the characteristic patterns of AU co-activation during centrifugal training. Experimental validation on centrifuge training datasets demonstrates strong performance, achieving an AUC-ROC of 0.898 and an AP score of 0.96, confirming the system’s ability to reliably identify characteristic patterns of AU co-activation during G-LOC events. Overall, this contact-free system offers an interpretable solution for rapid G-LOC detection, or as a complementary enhancement to existing aeromedical monitoring technologies. The non-invasive design demonstrates significant potential for improving safety in aerospace physiology applications without requiring modifications to current cockpit or centrifuge setups. Full article

Micro-expression recognition (MER), as an important branch of intelligent visual sensing, enables the analysis of subtle facial movements for applications in emotion understanding, human–computer interaction and security monitoring. However, existing methods struggle to capture fine-grained spatiotemporal dynamics under limited data and computational resources, making them difficult to deploy in real-world sensing systems. To address this limitation, we propose HMRM, a hybrid motion and region-fused Mamba network designed for efficient and accurate MER. HMRM enhances motion representation through a hybrid feature augmentation module that integrates gated recurrent unit (GRU)-attention optical flow estimation with a regional MotionMix enhancement strategy to increase motion diversity. Furthermore, it employs a grained Mamba encoder to achieve lightweight and effective long-range temporal modeling. Additionally, a regions feature fusion strategy is introduced to strengthen the representation of localized expression dynamics. Experiments on multiple MER benchmark datasets demonstrate that HMRM achieves state-of-the-art performance with strong generalization and low computational cost, highlighting its potential for integration into compact, real-time visual sensing and emotion analysis systems. Full article

A person’s face can reveal their mood, and microexpressions, although brief and involuntary, are also authentic. People can recognize facial gestures; however, their accuracy is inconsistent, highlighting the importance of objective computational models. Various artificial intelligence models have classified microexpressions into three categories: positive, negative, and surprise. However, it is still significant to address the basic Ekman microexpressions (joy, sadness, fear, disgust, anger, and surprise). This study proposes a Transformers-based machine learning model, trained on CASME, SAMM, SMIC, and its own datasets. The model offers comparable results with other studies when working with seven classes. It applies various component-based techniques ranging from ViT to optical flow with a different perspective, with low training rates and competitive metrics comparable with other publications on a laptop. These results can serve as a basis for future research. Full article

The global rise in cancer incidence and mortality represents a major challenge for modern healthcare. Although current screening programs rely mainly on histological or immunological biomarkers, cancer is a multifactorial disease in which biological, psychological, and behavioural determinants interact. Psychological dimensions such as stress, anxiety, and depression may influence vulnerability and disease evolution through neuro-endocrine, immune, and behavioural pathways, especially by affecting adherence to therapeutic recommendations. However, these dimensions remain underexplored in current screening workflows. This review synthesizes current evidence on the integration of biological markers (tumor and inflammatory biomarkers), psychometric profiling (stress, depression, anxiety, personality traits), and behavioural digital phenotyping (facial micro-expressions, vocal tone, gait/posture metrics) for potential early cancer risk evaluation. We examine recent advances in computational sciences and artificial intelligence that could enable multimodal signal harmonization, structured representation, and hybrid data fusion models. We discuss how structured computational information management may improve interpretability and may support future AI-assisted screening paradigms. Finally, we highlight the relevance of digital health infrastructure and telemedical platforms in strengthening accessibility, continuity of monitoring, and population-level screening coverage. Further empirical research is required to determine the true predictive contribution of psychological and behavioural modalities beyond established biological markers. Full article

Midline facial clefts are severe craniofacial defects that occur due to an underdeveloped frontonasal process. While genetic studies in mice have identified several genes that are crucial for midfacial development, the interactions and regulatory mechanisms of these genes during development remain unclear. In this study, we conducted a systematic review and database search to curate genes associated with midline facial clefts in mice. We identified a total of 78 relevant genes, which included 69 single-gene mutant mice, nine spontaneous models, and 20 compound mutant mice. We then performed bioinformatic analyses with these genes to identify candidate microRNAs (miRNAs) that may regulate the expression of genes related to midline facial clefts. Furthermore, we experimentally evaluated the four highest-ranking candidates—miR-320-3p, miR-381-3p, miR-27a-3p, and miR-124-3p—in O9-1 cells. Our results indicated that overexpression of any of these miRNAs inhibited cell proliferation through the suppression of genes associated with midline facial clefts. Thus, our results suggest that miR-320-3p, miR-381-3p, miR-27a-3p, and miR-124-3p are involved in the cause of midline facial anomalies. Full article